Re: Centering (was Re: Missing covariates)

From: "diane r mould" <drmould@attglobal.net> Subject: Re: Centering (was Re: Missing covariates) Date: Wed, 4 Jul 2001 18:05:23 -0400 Alan I rather doubt that a minor shift in a value used for centering or normalization would be as destructive as you indicate. I have never seen that happen, but would be quite interested to see examples. However I do believe strongly in the applicability of a model. If changing the value used for centering a variable makes it more reflective of the intended use of that model, I dont see that this is necessarily problematic. As far as I can tell, its not uncommon for a clinical trial to enroll patients with different distributions of demographics than the intended patient population. The purpose of centering is two-fold. One purpose is to help out numerically and if the choice of value used for centering is far from the median or mean value for that demographic covariate, then you will certainly lose that benefit of centering. So if your study is in pediatric patients and you chose 65 yrs as the value to center age with, that could cause problems such as causing parameter values to become negative, or making it difficult to achieve convergence. If the latter occurred or could not be overcome by appropriate parameterization, one might be inclined to disregard potential covariates. So to that extent, I would agree that if you pick a value for centering that is greatly different from your present study you could have problems with the modeling exercise. However, the second purpose is to get out parameter values for the typical patient that the drug is supposed to be used to treat. Therefore, if the mean age of a particular study is 50.938 and you chose this for your centering value, I doubt that the physicians who might have to use your formula will appreciate it, especially if most of their patients are 55 years old :-) Nick Holford's earlier comments to NMUSERS about routinely using a value of 70 to center creatinine clearance is just such an example of picking a user-friendly value. I dont agree that model utility and model development should be kept entirely separate - the end use of the model is the reason that the modeling work is done in the first place. We should always keep the final application in mind when doing this sort of work. The parameterization, covariate selection criteria and even the tests that one might use to qualify a model are all highly dependent on what you plan on doing with it. So using a median value, or a mean value is of equal value, as the primary purpose of centering is not statistical, its numerical. If the covariate model is appropriate to describe the effect of that covariate on a parameter, it doesnt really matter what value is used to help out numerically. As long as the number selected is reasonable, it makes little difference to NONMEM, only to the customer who must use the results. as to the questions that you asked: A). Does the (structural) model correspond to the mean values or median values of concentration data?=20 I am really not sure what is meant here. The structural model should reflect the data vs time profiles of all individuals, not the mean or the median of the data. The choice of the structural model depends on the individual data, and not the mean or the median values. The parameterization of the selected model depends to some extent on what the model is intended for. B). Does the structural model correspond to mean or median values of covariates?=20 Again, I am not sure what you are asking. The structural model does not depend on the median or the mean values of the covariates. The value of the parameters of that model may reflect these values however. Diane